Decentralized learning of energy optimal production policies using PLC-informed reinforcement learning

[1]  Dorothea Schwung,et al.  Distributed Self-Optimization of Modular Production Units: A State-Based Potential Game Approach , 2020, IEEE Transactions on Cybernetics.

[2]  A. Schwung,et al.  Optimierung in verteilten Produktionssystemen , 2020 .

[3]  Byung Jun Park,et al.  A model-based deep reinforcement learning method applied to finite-horizon optimal control of nonlinear control-affine system , 2020 .

[4]  Yanfei Sun,et al.  A Deep-Reinforcement-Learning-Based Optimization Approach for Real-Time Scheduling in Cloud Manufacturing , 2020, IEEE Access.

[5]  Dongda Zhang,et al.  Reinforcement Learning for Batch Bioprocess Optimization , 2019, Comput. Chem. Eng..

[6]  Mario Zanon,et al.  Data-Driven Economic NMPC Using Reinforcement Learning , 2019, IEEE Transactions on Automatic Control.

[7]  Steven X. Ding,et al.  ACTOR-CRITIC REINFORCEMENT LEARNING FOR ENERGY OPTIMIZATION IN HYBRID PRODUCTION ENVIRONMENTS , 2019, International Journal of Computing.

[8]  Dorothea Schwung,et al.  Self-Optimization in Smart Production Systems using Distributed Reinforcement Learning , 2019, 2019 IEEE International Conference on Systems, Man and Cybernetics (SMC).

[9]  R. Bhushan Gopaluni,et al.  Deep Reinforcement Learning for Process Control: A Primer for Beginners , 2019, AIChE Journal.

[10]  Jay H. Lee,et al.  Reinforcement Learning - Overview of recent progress and implications for process control , 2019, Comput. Chem. Eng..

[11]  Razvan Pascanu,et al.  Distilling Policy Distillation , 2019, AISTATS.

[12]  Steven X. Ding,et al.  Self Learning in Flexible Manufacturing Units: A Reinforcement Learning Approach , 2018, 2018 International Conference on Intelligent Systems (IS).

[13]  Sanyam Kapoor,et al.  Multi-Agent Reinforcement Learning: A Report on Challenges and Approaches , 2018, ArXiv.

[14]  Ronay Ak,et al.  A Survey of the Advancing Use and Development of Machine Learning in Smart Manufacturing. , 2018, Journal of manufacturing systems.

[15]  Dazhong Wu,et al.  Deep learning for smart manufacturing: Methods and applications , 2018, Journal of Manufacturing Systems.

[16]  Yee Whye Teh,et al.  Mix&Match - Agent Curricula for Reinforcement Learning , 2018, ICML.

[17]  Andrew Zisserman,et al.  Kickstarting Deep Reinforcement Learning , 2018, ArXiv.

[18]  Guy Lever,et al.  Value-Decomposition Networks For Cooperative Multi-Agent Learning Based On Team Reward , 2018, AAMAS.

[19]  Shimon Whiteson,et al.  Counterfactual Multi-Agent Policy Gradients , 2017, AAAI.

[20]  A. S. Xanthopoulos,et al.  Reinforcement Learning-Based and Parametric Production-Maintenance Control Policies for a Deteriorating Manufacturing System , 2018, IEEE Access.

[21]  Razvan Pascanu,et al.  Imagination-Augmented Agents for Deep Reinforcement Learning , 2017, NIPS.

[22]  Yee Whye Teh,et al.  Distral: Robust multitask reinforcement learning , 2017, NIPS.

[23]  Qian Li,et al.  A Survey of Recent Research on Optimization Models and Algorithms for Operations Management from the Process View , 2017, Sci. Program..

[24]  Yi Wu,et al.  Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments , 2017, NIPS.

[25]  Jonathan P. How,et al.  Deep Decentralized Multi-task Multi-Agent Reinforcement Learning under Partial Observability , 2017, ICML.

[26]  Dorian Kodelja,et al.  Multiagent cooperation and competition with deep reinforcement learning , 2015, PloS one.

[27]  Paulo Leitão,et al.  Industrial automation based on cyber-physical systems technologies: Prototype implementations and challenges , 2016, Comput. Ind..

[28]  Klaus-Dieter Thoben,et al.  Machine learning in manufacturing: advantages, challenges, and applications , 2016 .

[29]  Razvan Pascanu,et al.  Policy Distillation , 2015, ICLR.

[30]  Yuval Tassa,et al.  Continuous control with deep reinforcement learning , 2015, ICLR.

[31]  Shane Legg,et al.  Human-level control through deep reinforcement learning , 2015, Nature.

[32]  Sergey Ioffe,et al.  Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift , 2015, ICML.

[33]  Jimmy Ba,et al.  Adam: A Method for Stochastic Optimization , 2014, ICLR.

[34]  Joel J. P. C. Rodrigues,et al.  Metaheuristic Scheduling for Cloud: A Survey , 2014, IEEE Systems Journal.

[35]  Pedro Ferreira,et al.  An MDP Model-Based Reinforcement Learning Approach for Production Station Ramp-Up Optimization: Q-Learning Analysis , 2014, IEEE Transactions on Systems, Man, and Cybernetics: Systems.

[36]  Nitish Srivastava,et al.  Dropout: a simple way to prevent neural networks from overfitting , 2014, J. Mach. Learn. Res..

[37]  Enrico Zio,et al.  Reinforcement learning for microgrid energy management , 2013 .

[38]  Shalabh Bhatnagar,et al.  Natural actor-critic algorithms , 2009, Autom..

[39]  Tommaso Cucinotta,et al.  A Real-Time Service-Oriented Architecture for Industrial Automation , 2009, IEEE Transactions on Industrial Informatics.

[40]  Bart De Schutter,et al.  A Comprehensive Survey of Multiagent Reinforcement Learning , 2008, IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews).

[41]  Guillaume J. Laurent,et al.  Hysteretic q-learning :an algorithm for decentralized reinforcement learning in cooperative multi-agent teams , 2007, 2007 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[42]  Richard S. Sutton,et al.  Reinforcement Learning: An Introduction , 1998, IEEE Trans. Neural Networks.

[43]  Martin Lauer,et al.  An Algorithm for Distributed Reinforcement Learning in Cooperative Multi-Agent Systems , 2000, ICML.

[44]  Yishay Mansour,et al.  Policy Gradient Methods for Reinforcement Learning with Function Approximation , 1999, NIPS.

[45]  Ian H. Witten,et al.  An Adaptive Optimal Controller for Discrete-Time Markov Environments , 1977, Inf. Control..

[46]  P. Mazur.  On the theory of brownian motion , 1959 .

[47]  G. Uhlenbeck,et al.  On the Theory of the Brownian Motion , 1930 .